It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code.

In this course, we are going to build an end-to-end Python machine learning project. You’ll learn how to use Keras to build and tune a deep neural network.

Keras is quickly becoming the de facto tool to do deep learning in Python, especially for beginners. Its minimalist, modular approach makes it simple to get deep neural networks up and running.

A Jupyter notebook is a web app that allows you to write and annotate Python code interactively. It’s a great way to experiment, do research, and share what you are working on.

In this course all of the tutorials will be created using jupyter notebooks. In the preview lessons we install Python. Check them out. They are completely free.

We will also gently introduce you to the vernacular of deep learning. For example, a deep neural network is simply a neural network with more than one hidden layer. That’s it.

Actually, a hidden layer really means “not an input or an output.”

Why all the hype around deep learning? While much of the hype in the IT world is just that, the hype around deep learning may be the real thing. Recently, deep learning models have been outperforming every other kind of machine learning model.

You’ll get hands on experience with the process of machine learning. The process involves importing data, cleaning the data, training and testing, pre-processing and feature engineering.

We are going to define new terms but we will skip the math and theory for now.

Thanks for your interest in A Gentle Introduction to Deep Learning Using Keras.